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        <tabi:current_section>ollama</tabi:current_section>
    </tabi:metadata><link rel="extra-stylesheet" href="https://blog.sagamiyun.me/skins/teal.css?h=bd19e558a52d678a50de" /><title>Kit Kyo - ollama</title>
        <subtitle>Full-stack engineer · DevOps · agent development — currently going deep on ML &amp; infrastructure.</subtitle>
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    <generator uri="https://www.getzola.org/">Zola</generator><updated>2026-06-15T00:00:00+00:00</updated><id>https://blog.sagamiyun.me/tags/ollama/atom.xml</id><entry xml:lang="en">
        <title>Your edge LLM can&#x27;t tell time — and a 40-line memory can fix it (for some models)</title>
        <published>2026-06-15T00:00:00+00:00</published>
        <updated>2026-06-15T00:00:00+00:00</updated>
        <author>
            <name>Kit Kyo · A2O Labs</name>
        </author>
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        <summary type="html">A tiny adversarial benchmark shows small local LLMs confuse token-distance for elapsed wall-clock time. A zero-dependency continuous-time memory fixes it for sub-millisecond cost — but whether a model will even accept that fix turns out to be a model-dependent deployability property that capability leaderboards don&#x27;t measure.</summary>
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